Really valuable post. I'm building a much simpler job alert pipeline — fetching emails, filtering with Claude, logging to CSV — and even at that small scale several of these landed directly. The silent failure point especially: I realised my watcher process has no supervisor, so if it dies quietly I just miss job alerts with no indication anything is wrong. Also the 'I cannot as a hypothesis' reframe is already changing how I handle Claude refusing to classify an ambiguous job listing. Thanks for writing the failures, not just the wins.
Mistake 6 hit me hardest. I teach teenagers cognitive sovereignty — how to own their reasoning before they touch AI tools — and “I can’t” is the exact failure mode I see in students every week. Not because they actually can’t. Because the cost of trying feels higher than the cost of quitting. You called it character drift. I call it the Internal Enforcement Model collapsing. Same pattern, different altitude.
Your one-sentence summary — “every one of them started with an assumption that had stopped being true” — is the cleanest description I’ve seen of what I call interaction debt. Students build on stale assumptions the same way your agent built on stale memory. Small drift, invisible for weeks, then the whole structure fails under load. The fix in both cases isn’t more tools. It’s honest self-monitoring before the next action.
The rightsizing principle is real too. I teach kids to treat their working memory the way you treat your Mac Mini’s resource budget — as a finite thing you refuse to silently overdraw. Most of them have never been told that bandwidth is real and has limits. They just assume they’re broken when they hit the wall.
Interesting that you are not directly comparing AI to students, because I think this is actually very accurate for what we are doing with AI agents. We are trying to teach them, educate them in the ways that we want them to operate.
Really valuable post. I'm building a much simpler job alert pipeline — fetching emails, filtering with Claude, logging to CSV — and even at that small scale several of these landed directly. The silent failure point especially: I realised my watcher process has no supervisor, so if it dies quietly I just miss job alerts with no indication anything is wrong. Also the 'I cannot as a hypothesis' reframe is already changing how I handle Claude refusing to classify an ambiguous job listing. Thanks for writing the failures, not just the wins.
Mistake 6 hit me hardest. I teach teenagers cognitive sovereignty — how to own their reasoning before they touch AI tools — and “I can’t” is the exact failure mode I see in students every week. Not because they actually can’t. Because the cost of trying feels higher than the cost of quitting. You called it character drift. I call it the Internal Enforcement Model collapsing. Same pattern, different altitude.
Your one-sentence summary — “every one of them started with an assumption that had stopped being true” — is the cleanest description I’ve seen of what I call interaction debt. Students build on stale assumptions the same way your agent built on stale memory. Small drift, invisible for weeks, then the whole structure fails under load. The fix in both cases isn’t more tools. It’s honest self-monitoring before the next action.
The rightsizing principle is real too. I teach kids to treat their working memory the way you treat your Mac Mini’s resource budget — as a finite thing you refuse to silently overdraw. Most of them have never been told that bandwidth is real and has limits. They just assume they’re broken when they hit the wall.
Interesting that you are not directly comparing AI to students, because I think this is actually very accurate for what we are doing with AI agents. We are trying to teach them, educate them in the ways that we want them to operate.